Basic Robotic Behaviors and Odometry
Explore autonomous mobile robotics through hands-on simulation exercises, covering motion planning, control systems, and real-world implementation strategies.
Explore autonomous mobile robotics through hands-on simulation exercises, covering motion planning, control systems, and real-world implementation strategies.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Introduction to Robotics with Webots Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Model robotic mechanisms in physics-based simulation
Implement basic reactive and discrete controllers
Perform forward kinematics computations
Design and test autonomous navigation systems
Develop sensor-based control algorithms
Skills you'll gain
This course includes:
5 Hours PreRecorded video
12 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course introduces students to autonomous mobile robotics through practical simulation using Webots. The curriculum covers essential topics including forward kinematics, sensors and actuators, and reactive behaviors. Students learn to model mechanisms in a physics-based environment, implement basic controllers, and understand non-holonomic systems. Through hands-on exercises, learners develop skills in robot localization, coordinate transforms, and motion planning, with emphasis on understanding how physical systems impact algorithm design.
Getting Started
Module 1 · 6 Hours to complete
Sensors and Actuators
Module 2 · 5 Hours to complete
Reactive Behaviors and State Machines
Module 3 · 4 Hours to complete
Coordinate Systems, Degrees of Freedom and Forward Kinematics
Module 4 · 5 Hours to complete
Localization with Odometry and Loop Closure
Module 5 · 5 Hours to complete
Fee Structure
Instructors
Professor of Computer Science
Nikolaus Correll is a Professor of Computer Science at the University of Colorado Boulder, where he has been a faculty member since 2009. He previously served as a post-doctoral associate at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT). Nikolaus earned his Ph.D. in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL) in 2007 and holds a degree in Electrical Engineering from Eidgenössische Technische Hochschule (ETH) Zurich, obtained in 2003.His research focuses on reasoning under uncertainty in robotic manipulation and swarm robotics, areas that are critical for advancing autonomous systems. Nikolaus has received several prestigious awards, including the NASA Early Career Faculty Fellowship in 2012, the National Science Foundation's CAREER award, and the Provost Achievement Award at CU Boulder in 2016. He is recognized for his contributions to the field through keynote talks at international conferences and has received multiple best paper awards. At CU Boulder, he teaches courses such as "Basic Robotic Behaviors and Odometry," "Robotic Mapping and Trajectory Generation," and "Robotic Path Planning and Task Execution," equipping students with essential skills in robotics and automation. Through his work, Nikolaus Correll continues to shape the future of robotics research and education.
Assistant Professor
Alessandro Roncone is an Assistant Professor at the University of Colorado Boulder, where he leads the Human Interaction and Robotics (HIRO) Group. His research focuses on human-robot interaction, artificial intelligence, and robot control and planning, aiming to develop technologies that facilitate natural and effective cooperation between humans and robots. He obtained his Ph.D. in Robotics, Cognition, and Interaction Technologies from the Italian Institute of Technology in 2015, following a B.Sc. in Biomedical Engineering and an M.Sc. in NeuroEngineering.Before joining CU Boulder in 2018, Roncone was a postdoctoral associate at Yale University, where he worked on human-robot collaboration and advanced manufacturing. His expertise includes kinematics, optimization, decision-making under uncertainty, and tactile sensing. He has published extensively in top-tier robotics journals and conferences, contributing significantly to the field through both research and teaching. At CU Boulder, he teaches courses such as "Basic Robotic Behaviors and Odometry," equipping students with foundational skills in robotics. Through his work, Alessandro Roncone continues to push the boundaries of how robots can assist and collaborate with humans in various environments.
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